Data Analysis Methods And Analysis Essay

red-light safety cameras increased 135%.” (“Red-Light Running Dangers”) When the conversation of whether to have red light cameras, or not to have red light cameras arises, human fatalities should without a doubt be a top consideration. Especially when a study performed during 2011 by the Insurance Institute for Highway Safety found that red light cameras had lowered

Death among U.S. teens is led by vehicle crashes. In the year of 2013, around 2,163 teens were killed between the ages of 16-10 in the United States. Around 243,243 were treated in emergency departments for injuries that they suffered in motor vehicle accidents. Around 75% of teen drivers suffer in these accidents due to 3 major mistakes. Those major mistakes include a lack of scanning the road, distractions in the vehicle, and also going to fast for the conditions of the road.

Results: As we are able to gather from both of these graph which came from U.S. Department of Transportation. We are currently slightly down on DUI’s, but the part that I have found to be even more shocking in a good way that fatalities not involving alcohol has drop almost 10,000. While this graph only shows the amount of crashes it does not put it in as detailed as the next graph does.

Each year numerous lives are lost due to careless and irrational driving. The disregard for safe driving has been a predicament to the United States of America for years. Many years Police have relied heavily on speed cameras, breathalyzer tests and heavy fines as a deterrent against unlawful drivers. Over the years fatality rates have increased, so the Department of Transportation and Highway Safety has composed a series of safe driving campaigns. On many occasions the Transportation Department informs and advises the public about the importance of responsible driving. They propagate safe driving through the various channels of the media and

drivers 16-20 years who were involved in fatal crashes, and were intoxicated, dropped 47 percent from 23 percent in 1985 to 12.7 percent in 1995.” (Teens and Drunk Driving) Each day

It is important to note that when the authors analyzed data from this source they did not separate fatal accidents involving drunk driving from accidents that did not pertain to drunk driving as to minimize the influence of outside factors like the bias of a police officer. The authors then used this data to look at trends in fatal accident rates on both Sunday’s and weekdays in states that either repealed their blue laws between 1900 and 2009, states that still had blue laws during that time period and states that either never had blue laws or repealed prior to 1990. Upon looking at these trends, the authors did not discover any major changes in the fatal accident rates on Sunday in states that repealed their Blue laws. The only notable evidence to support a claim that the repeal of Blue laws leads to more fatal accidents on Sunday was the fact that in repeal states Sunday crash rates “remained constant until 2006” while their weekday fatal accident crash rates had decreased in the late 1990’s (2011, 807-8). Yet this does not suggest that the repeal of Blue laws caused this as the same relative trend occurred in states that never had Blue laws which signifies that the trend was not caused by the repeal of Blue laws. Additionally, in repeal states their fatal accident rates on both Sunday and on weekdays had a general downward trend with the only difference being that the weekdays fatal accident rate decreased at a slightly stronger rate. This led the authors to conclude that Blue laws have “at most a small effect on fatal accidents” as there were no significant changes in the fatal accident rates to support that claim (2011, 808). Finally, the authors used the same data set to demonstrate that the repealing of Blue laws did not have any significant effect on the fatal accident rates

I wish to replicate the methodology utilized by Ariel, Farrer, and Sutherland (2015). These researchers conducted a randomized-controlled study with the Rialto Police Department in California. The purpose of their study was to observe the effects of body-worn cameras on the frequency of use-of-force incidents as well as citizens’ complaints. In my research study, I will test only the effect of body-worn cameras on use-of-force incidents. I propose to study the Indianapolis Metropolitan Police Department in Indianapolis, Indiana. This research setting is a lot larger than Ariel, Farrer, and Sutherland’s study. The Rialto Police Department covered an area of approximately 28.5 square miles, and they served a population of approximately 100,000 residents. IMPD covers an area of approximately 364.3 square miles, and serves approximately 824,000 residents. Rialto has 115 sworn officers whereas IMPD has 1,080 sworn officers (IMPD n.d.).

With the increasing emergence of traffic cameras around the nation, there has emerged a debate about whether the cameras are effectively functioning to keep drivers safe or whether they are just another source of revenue for cites. Facts have proven that the purpose for the traffic cameras is simply generating more money for the cities. The estimated amount of money that the city of Denver will be making in 2011 from these cameras (Kaminsky)—excluding the ones recently put up—a grand estimation total of seven million dollars… “According to the Insurance Institute for Highway Safety more than 550 communities in the United States use red light cameras.” (Urie) Research shows how the cameras are not improving safety for drivers, and for

Motor vehicle crashes has continued to be one of the leading causes of death and injury to RC residents. According to the California Highway Patrol Statewide Integrated Traffic Records System (SWITRS),

Red light running is a serious issue in the United States. According to T. Walden and B. Bochner (2011), it is estimated these violations result in over 100,000 accidents and 1,000 fatalities each year. The economic impact is assessed to be over $14 billion dollars annually (“Effectiveness of Red Light Cameras-Texas Statewide Evaluation,” p. 30). Typical costs include items such as property damage, medical expenses, response expenditures, etc. The monetary costs are significant, but the loss of life is incalculable. N. Elminity and E. Radwan (2008) state that drivers who run red lights are the primary cause of high-speed angular collisions at intersections (“Issues Related to Red-Light Camera Enforcement Systems,” p. 32). RLCs are an attempt to increase safety and reduce risk at problematic intersections in order to mitigate these costs.

Vanguard Attorneys: Florida Injury Firm popped up, and I expected to find nothing but money-centered conversation there. In this article, "Red-Light and Speed Enforcement Cameras – Debated Pros and Cons" written by the firm’s managing attorney, Elizabeth Munro, was a pros-and-cons chart, three bullet points for both. Could this be it, the plus side of RLC? Unfortunately, no. The cons were the usual accusation of a moneymaking scheme priority over public safety. The pros included were as follows: accidents are less frequent at these intersections than the average intersection, traffic law violations drop significantly in areas with these cameras, and pedestrian deaths have decreased at intersections with these cameras. I found these statements contradictory to my first sources, namely because other states have reported that these intersections are more prone to accidents. I guess Florida has just been lucky. The only real information I gained was a small point in the intro to the chart--RLC are banned in 12 states. Interesting, isn't

The resulting pursuit driver training programs will lead to an exchange of information, an increase in knowledge and the development of skills necessary for law enforcement officers to successfully conduct vehicular pursuits. They are making many different programs for people, including the guide: Strengthening the Citizen and Police Partnership at the Traffic Stop: Professionalism is a Two-Way Street This guide is for law enforcement agencies to assist them in conduct traffic stops in a professional manner, to enhance public relations and image of the law enforcement agency, maintain the credibility of the law enforcement agency and to minimize the number of complaints. 1999 Crime-Clock this graphic was developed to compares the incidences of traffic crashes that injure and kill motorists to the assaults and deaths associated with crime in a "snapshot" format. You Drink & Drive, You Lose. America's New Impaired Driving Campaign A new and comprehensive impaired driving prevention program for states and communities to use in reach the national goal of reducing alcohol-related deaths to no more than 11,000 by the year 2005. The campaign targets high-risk populations such as 21- to 34-year-olds, high blood alcohol and repeat offenders, and underage drinkers by increasing public education, expanding public-private partnerships, enacting strong

The behavior was observed from a large sample of people. This included twenty people shopping alone and twenty people shopping in a group, for a total of forty subjects, each ranging in ages from approximately 19 to 40 years old. The subjects were observed from the time they entered the store until the time they left. The reasoning behind this was to see the buying behavior differences for the whole experience and figure out which areas of the store could be improved in order to appeal to groups of people shopping. The behaviors observed included the amount of time spent inside the store, the amount of money spent, the interactions with associates, and the areas of the stores that were navigated.

The ultimate goal of this study was to compare Poisson and Negative binomial regression models using road accident (count) data to assess which of them significantly fit count data. The data for this study were obtained from the Building and Road Research Institute of the Council for Scientific and Industrial Research (CSIR) on the number of people killed by road accidents in Ghana, including days that those accidents occurred within the period from 2001-2010.The maximum likelihood estimation was employed to determine the parameter estimates for each model with R statistical package. The results showed that the negative binomial regression model best fits road accidents’ data significantly as compared with the poison regression model. The results also revealed that the day an accident occurred within the week significantly affected the expected number of people killed in that accident. It was identified that Saturday significantly had the highest number of people killed in road accidents in Ghana.

The objective of this chapter is to describe the procedures used in the analysis of the data and present the main findings. It also presents the different tests performed to help choose the appropriate model for the study. The chapter concludes by providing thorough statistical interpretation of the findings.